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Central Nervous System Stimulants

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Physicochemical characterization of an arabinoxylan-rich fraction from brewers' spent grain and its application as a release matrix for caffeine.

Food research international (Ottawa, Ont.)
The brewers' spent grain is a by-product generated during brewery process and is a potential source for arabinoxylans (AX) extraction. In the present work, the extraction and characterization of an arabinoxylan-rich fraction from brewers' spent grain...

Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop sp...

[Identification of Methamphetamine Abuse and Selegiline Use: Chiral Analysis of Methamphetamine and Amphetamine in Urine].

Fa yi xue za zhi
OBJECTIVES: To study the content variation of selegiline and its metabolites in urine, and based on actual cases, to explore the feasibility for the identification of methamphetamine abuse and selegiline use by chiral analysis.

Multimodal treatment efficacy differs in dependence of core symptom profiles in adult Attention-Deficit/Hyperactivity Disorder: An analysis of the randomized controlled COMPAS trial.

Journal of psychiatric research
There is broad consensus that to improve the treatment of adult Attention-Deficit/Hyperactivity Disorder (ADHD), the various therapy options need to be tailored more precisely to the individual patient's needs and specific symptoms. This post-hoc ana...

Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.

The international journal of neuropsychopharmacology
BACKGROUND: There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographi...

Neonatal brain inflammation enhances methamphetamine-induced reinstated behavioral sensitization in adult rats analyzed with explainable machine learning.

Neurochemistry international
Neonatal brain inflammation produced by intraperitoneal (i.p.) injection of lipopolysaccharide (LPS) results in long-lasting brain dopaminergic injury and motor disturbances in adult rats. The goal of the present work is to investigate the effect of ...

Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study w...

Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.

Computers in biology and medicine
OBJECTIVE: Given the significantly increased number of individuals prescribed stimulants in the past decade, there has been growing concern regarding the risk of cardiovascular events among adults on stimulant therapy. We aimed to quantify the added ...